A new approach to aggregating reasoning from multiple LLM agents shows that analyzing complete reasoning traces rather than relying on majority voting leads to more accurate solutions. This method, called Self-Consistent Mixture of Agents, enhances performance across various complex problem-solving domains by leveraging trace diversity.